The Costs of Sprawl Reconsidered: What the Data Really Show

Over
the past several years there has emerged in the United States an
influential political movement whose purpose is to severely limit,
or even prohibit, further suburbanization. This "anti-sprawl"
movement has received much attention and has been successful in
implementing its restrictive land-use policies in some areas. Much
of the justification for the current campaign against the
low-density (sprawling) urban development that Americans and
Western Europeans1
prefer is based upon assumptions that it is more costly than the
more dense development of central cities.

Variously described as "smart growth,"
"growth management," or "New Urbanism," the movement would force
people to live at higher densities, in multi-family units,
townhouses, or clustered single-family developments--while placing
significant restrictions on the expansion of suburban commercial
development

The
rationales offered for limiting suburban housing choices are many,
various, and of questionable validity. At one point or another over
the past half-decade, critics of suburban development have cited
its adverse impact on "food security," wildlife, and air and water
quality. Critics of suburban expansion even contend that suburbs
contribute to serial killings, teenage angst, social alienation,
low wages, obesity, asthma, and higher taxes. This last item, the
belief that lower-density, "more sprawling" development fuels
higher government expenditures, is the most common reason elected
officials in many municipalities adopt measures to limit housing
growth in their communities.

Typical of the concern that low-density
development raises municipal costs--and therefore local taxes--is a
contention in a recent, federally funded study of sprawl and costs
that claims the United States "no longer can pay for the
infrastructure necessary to develop farther and farther out in
metropolitan areas."2

Current Urban
Planning AssumptionsThe U.S. urban planning community has adopted several
assumptions about suburbanization and local government
expenditures. These are outlined below and are referred to as the
Current Urban Planning Assumptions in this paper.

Lower spending
per capita will be associated with higher population
densities. Thus, it is presumed that the more densely
developed a community is, the less costly it will be to provide
government services on a per capita basis. Conversely, the more
widely dispersed development is (as in a community in which houses
are spread out on large lots), the higher will be local government
expenditures per capita.

Lower spending
per capita will be associated with lower rates of population
growth. This is based upon the belief that the burden of
building new infrastructure in newer, growing communities is
greater than it would be to expand or use latent capacity in older,
slower-growing communities.

Lower spending
per capita will be associated with older municipalities.
It is assumed, for example, that the existing infrastructure of
older municipalities has latent capacity, can be expanded, or can
be used more intensively for less than the costs of building
infrastructure in newer, more sprawling municipalities (which are
virtually always suburban). At least partially as a result of this
belief, current urban planning theory places a priority on
construction within highly developed areas (referred to as "infill"
development) instead of in undeveloped areas (referred to as
"greenfield" development).

Belief in these assumptions provides
support to urban planners and others who are interested in limiting
suburban development and, in extreme cases, outlawing development
outside "urban growth boundaries" or designated "growth areas."

In
fact, however, virtually all of the research on which the Current
Urban Planning Assumptions are based is theoretical, projecting
relative costs into the future without examining the actual
expenditures that are being made today by municipalities of
differing urban forms and ages. The analysis in this paper reviews
actual municipal expenditure data in relation to the Current Urban
Planning Assumptions. Among the findings:

Based upon an econometric analysis of data
from the year 2000 for more than 700 municipalities, we conclude
that none of the Current Urban Planning Assumptions is associated
with any practically significant variation in local government
expenditures per capita. In addition, the econometric analysis is
able to account for less than 30 percent of the variation in local
government expenditures per capita. This indicates that other
factors, not accounted for in the econometric formula, are more
important.

Based upon a nominal (ranking) analysis of
the same dataset, we conclude that the Current Urban Planning
Assumptions are almost 180 degrees opposite the reality of
municipal expenditures. The highest density municipalities have
higher than average expenditures per capita; the slowest growing
municipalities have higher than average expenditures per capita;
and the oldest municipalities have the highest expenditures of all
per capita.

By
far the largest expenditure for municipal governments is employee
compensation. There were no reliable data for including this
variable in the econometric analysis. Yet our nominal analysis
indicates that virtually all of the variation between municipal
expenditures per capita can be accounted for by differences in
employee compensation per capita.

Costs of Urban Sprawl: Research

Perhaps the most oft-quoted recent
research attempting to estimate the relationship between sprawl and
infrastructure costs was conducted by a team led by Professor
Robert Burchell and funded under the auspices of the federal
government's Transit Cooperative Research Program (TCRP). The
project included two reports: Costs of Sprawl--2000 and The Costs
of Sprawl--Revisited.3 The Costs of Sprawl--2000 projected
that from 2000 to 2025, America would incur $227.4 billion in gross
additional costs for what the study terms "uncontrolled growth"
(less dense, more sprawling growth) versus "controlled growth"
(more dense, less sprawling growth). This equates to approximately
$9.1 billion in gross additional costs per year.

The
figure of $227.4 billion may seem large.4 Yet in the context of 25 years and an
average population of 115 million households, it is actually rather
modest. The $227.4 billion would amount to only $80 per household
annually, or $29 per capita. (See Table 1.) This includes:

$4.41 per household (or $1.63 per capita)
for additional sewer and water costs;

$38.37 per household (or $14.16 per
capita) for additional roadways; and

$36.77 per household (or $13.57 per
capita) for expanded public services.

Many
growth critics have used these estimates as a measure of the cost
that sprawl imposes on society. Advocates of smart growth policies
have implied that these estimates represent the costs of continued
low-density development on society. However, many of the purported
costs are not imposed on society at all: They are private costs
freely paid by the people who buy new houses.

The
cost of sewer and water infrastructure in new developments is
passed on to the buyer, and subsequent operation is typically
funded with fees assessed on residents and businesses based upon
use. Typically, new housing development infrastructure (local
streets, curbs, sidewalks, storm and waste sewers, and water supply
lines within the development) is paid for privately by the
purchasers of new houses, having been built by developers or
homebuilders. These are fully private costs that are paid for by
persons who voluntarily move into new houses and apartments, having
determined that they can afford such a move.

There are further indications that the projections
from the Costs of Sprawl--2000 are not "unaffordable," and actually
are modest in comparison to other costs in the economy.
Specifically:

From 1980 to 2000 (inclusive), the
increase alone in total personal income in the United States was
nearly $27 trillion (in 2000 dollars), or $1.3 trillion per year.5 This is more than 140
times the $9.1 billion in average annual additional costs projected
in Costs of Sprawl--2000 for 2000 through 2025.6

From 1980 to 2000 (inclusive), the total
increase in local government expenditures in the United States was
$4.5 trillion (in 2000 dollars adjusted for the increase in
population), or $225 billion per year. This is approximately 25
times the $9.1 billion average additional public and private costs
projected in Costs of Sprawl--2000.7

Municipal Expenditures: Econometric
Analysis

Because the authors of the Costs of
Sprawl--2000 mix public and private expenditures that they claim
are related to sprawl, the study's findings offer little guidance
on one of the key public policy issues related to suburbanization:
What are the actual, additional municipal costs that
suburbanization imposes on the community at large, if any? This
report will attempt to fill that void by conducting an econometric
analysis (see box) of municipal spending patterns to determine what
portion of municipal costs appear to be related to the impact of
sprawl.

If
the Current Urban Planning Assumptions are valid, the trends that
Costs of Sprawl--2000 identifies--having been underway for at least
five decades--should reveal clearly the differences in expenditures
between less sprawling and more sprawling areas. This means that
older, higher density municipalities should have lower costs per
capita than newer, lower density, more sprawling areas. These
differences should be evident in the present spending patterns of
local governments.

For
an issue that has galvanized public debate in many communities
throughout the country, there is little comprehensive, academic
research on the actual relationship between land-use patterns and
local government costs. The most recent research was published some
time ago and is based upon early 1980s data. It was conducted by
Professor Helen Ladd at Duke University, who performed an
econometric analysis of growth measures and the actual public
expenditures of 247 counties. She found that per capita
expenditures on public services tend to rise as density rises and
that higher population growth is associated with lower per capita
local government expenditures--precisely the opposite of Current
Urban Planning Assumption #1, above.8

Because the data used in her study are now
more than two decades old, there is a need for more contemporary
research on the factors that drive local government expenditures,
especially in view of the predominant influence of "smart growth"
urban planning theories. The purpose of this paper is to fill that
gap with municipal cost data drawn from the 2000 Census.

Source of
DataAlthough the term "sprawl" has no precise definition, its
most fundamental characteristic is lower population density. Smart
growth advocates presume that building less sprawling, higher
density communities results in lower government expenditures. If
this is indeed the case, an analysis of municipal spending patterns
across the country should show that as population densities go up,
costs go down--and vice versa.

In
order to reliably capture the impact of density on local government
spending, we analyzed data for municipalities (cities and towns)
rather than counties9
because that is the level of government most affected by finance
issues regarding utilities (wastewater and water) and general
public services. The source of municipal financial data for the
analysis in this paper is the U.S. Census Bureau government finance
database for fiscal year 2000, which contains information for
approximately 1,800 municipalities. Additional data for
municipalities are available from other sources, such as the 2000
U.S. Census (demographic and density data) and the U.S. Department
of Justice (crime rates). Another advantage of using municipal
(rather than county) data is that municipal boundaries typically
contain little rural space; therefore, the population density
within those boundaries is generally similar to urban population
density.

Because the current urban planning debate
is principally focused on where development occurs within the
nation's largest metropolitan areas, the municipalities analyzed in
this study included only those within metropolitan areas of more
than 1,000,000 residents in 2000.10 Consolidated city-counties were not
included, because such municipalities provide both city and county
services and would be expected to have inherently higher
expenditures as a consequence.11

The
analysis in this paper does not include primary and secondary
education costs. Most of the nation's primary and secondary
education is provided by independent school districts that seldom
match municipal (or county) boundaries. As a result, there is
little, if any, broad demographic data specific to the geographical
areas served by such districts. Related research indicates that,
contrary to Current Urban Planning Assumption #2, elementary and
secondary education expenditures tend to be lower in school
districts with the greatest enrollment growth, and highest where
there is the least growth.12 Our research focuses on municipal
costs in three categories:

Government expenditures (all costs except
for utilities and education);

Municipally owned wastewater utility
charges; and

Municipally owned water utility
charges.

Econometric
ModelsFor purposes of this study, three econometric models13 were developed to
estimate the relationships between various factors and municipal
expenditures:

The General Government Model was developed
to estimate the relationship between municipal current expenditures
per capita and growth-influencing factors;

Wastewater Charges Model; and

Water Utility Charges Model.

The
Wastewater Charges and Water Utility Charges models were developed
to capture the impact of density, growth, and age of community on
the cost of these functions. These are frequently cited in the
urban planning literature as being upwardly affected by more
sprawling development.

Another reason for analyzing utility
charges separate from general government functions is that water
and wastewater services are generally financed by user fees, rather
than by the general tax revenues that finance most other municipal
government operations. In fact, these utilities are not inherently
government services: In many communities, regulated private
companies provide such functions.14

General Government Model

The
General Government Model estimates the impact of a number of
factors on per capita municipal government expenditures, excluding
utilities and education. The model uses 13 factors that would be
expected to influence local government expenditures (current and
capital expenditures)15 per capita. These include
factors that test the Current Urban Planning Assumptions
(population density, population growth, and community age as
indicated by median house ages). There were sufficient data for 738
municipalities to be included in the General Government Model.16 Table 2 lists the
variables included in the General Government Model.

General
Government Model ResultsThe results indicate that the 13 factors in the General
Government Model explain approximately 29 percent of the variation
in municipal expenditures, as revealed in Table 3.17 This means that 71
percent of the variation in total expenditures is not explained by
the factors included in the model, but rather by other influences
which cannot be quantified or for which there are no available or
accurate data. The conclusion is that, contrary to the theory,
comparatively little of the variation in municipal costs is
associated with the Current Urban Planning Assumptions. Other
factors, which have not been identified, are more important.

As
Table 3 reveals, 8 of the 13 factors were reliable predictors of
either higher or lower municipal spending per capita (at a
statistically significant 95 percent level of confidence). These
factors are: poverty rate, local/state expenditure ratio, state and
federal aid, density, persons per household, owner-occupied housing
(percent of housing units occupied by owners rather than renters),
median house value, and crime rate.

As
the discussion in the box indicates, statistical significance does
not necessarily denote practical significance. A factor may be a
reliable predictor of an impact, but the impact itself may be
small. Among the 13 growth-related factors analyzed in the General
Government Model, practical significance varied widely. The
local-to-state expenditure ratio18 had the highest practical significance
(a 100 percent increase in the ratio of local government spending
to total state and local government spending would be associated
with a 55 percent increase in per capita expenditures--a practical
significance of 55 percent). The other variables with comparatively
high practical significance were persons per household (-42
percent), owner-occupied housing (-30 percent), crime rate (+25
percent) and median house value (+25 percent). The other reliably
predictive (statistically significant) factors had practical
significance less than 15 percent.19

Population
DensityThe results derived from the General Government Model are
consistent with the Current Urban Planning Assumption #1 that
associates higher densities with lower municipal government
expenditures--but only weakly so. The relationship was
statistically significant (99 percent), but there was little
practical significance, which would indicate that higher population
density is associated with only a small downward variation in
municipal costs per capita. The mathematical significance or
elasticity was 0.146: Each 10 percent increase in density could be
expected to produce a 1.46 percent decrease in municipal
expenditures per capita. For the average municipality, each 1,000
increase in population per square mile20 is associated with a $43 per capita
reduction in municipal expenditures. This is a minuscule
expenditure decrease compared with the substantial increase in
density required to trigger it. In other words, a virtually
unprecedented increase in population density in an already
urbanized area would trigger an decrease in expenditure equal to
the price of dinner for two at a moderately priced restaurant.21

Population
GrowthPopulation growth, the factor associated with Current
Urban Planning Assumption #2 was not statistically significant and
could not therefore be practically significant. Thus, the results
from the model do not support Current Urban Planning Assumption #2,
indicating no significant relationship between higher population
growth and higher municipal expenditures per capita.

Median House
AgeMedian house age, the factor associated with Current Urban
Planning Assumption #3 was not statistically significant and could
not therefore be practically significant. Thus, the results from
the model do not support Current Urban Planning Assumption #3,
indicating no significant relationship between newer communities
and higher municipal expenditures per capita.

Interestingly, the inclusion of the three
factors that measure the impact of the Current Urban Planning
Assumptions add little to the explanatory value of the General
Government Model as here specified. Only one--population
density--was found to be statistically significant (and of little
practical significance). Excluding these three variables
(population density, population growth, and median housing age) and
re-running the model with the remaining ten factors yields an
R-squared of 0.24, meaning that the model as so specified explains
only 24 percent of the measured expenditure variability from one
municipality to another. Adding the three growth-related variables
to these ten factors brings the R-squared up to only 0.29, meaning
that the inclusion of the growth variables improves the explanatory
value of the model by only five percentage points. This is not much
of an impact for issues that are alleged to be having important
effects on government costs in growing communities.

Wastewater Charges and Water Charges
Models

Neither the Wastewater Charges Model nor
the Water Utility Model indicated strong relationships between the
identified factors and user charges, as Tables 8 and 9 demonstrate
(see Appendix). The Wastewater Charges Model explained 12 percent
of the variation in wastewater user charges per capita, while the
Water Charges Model explained 8 percent of the variation in water
charges per capita.22
Thus, the Wastewater Charges Model failed to explain 88 percent of
the variation in wastewater charges, and the Water Utility Model
failed to explain 92 percent of the variation in water charges.
This suggests that influences other than those variables included
in the model explain much of the differences in utility costs from
one community to another.

With
respect to the Current Urban Planning Assumptions, only density was
found to be statistically significant, but of little practical
significance. In the Wastewater Charges Model, density exhibited a
practical significance of minus 18.0 percent, consistent with
Current Urban Planning Assumption #1. Similarly, density's
practical significance of minus 12.5 percent in the Water Utility
Model was consistent with Current Urban Planning Assumption #2
(Tables 2, 8, and 9). However, this translates into only small
impacts on consumer costs. A 1,000 person-per-square-mile
difference in average population density is associated with a $6
difference in annual wastewater charges per capita, or fifty cents
per month. In other words, a 1,000 person-per-square-mile
difference is associated with an annual water charge difference of
$4 per capita, or thirty-three cents per month--less than a penny
per day. Obviously, such trivial savings in waste water and
water-related costs cannot justify public policies that would force
major changes in existing lifestyles or land-use patterns.

It
is particularly significant that none of the Current Urban Planning
Assumptions were associated with a statistically significant
relationship with the variation in Wastewater Charges or Water
Charges. These infrastructure functions are among those cited most
often in claims that suburbanization imposes additional costs.

Alternative Measures of Relationship: A
Nominal Ranking Analysis

Another way to analyze the same data is to
rank it by categories that reflect varying degrees of difference in
some of the key independent variables (such as density) and relate
these categories to the different cost measures that comprise the
key dependent variables. The existence or absence of any obvious
trends indicates how strong or weak the relationships are. Using
the same Census data, a nominal (ranking) analysis by quintiles (20
percent rankings) was performed on the sample to determine whether
the statistical relationships that the Current Urban Planning
Assumptions would predict are actually evident in the data (Table
4).

As
the nominal rankings reveal, none of the growth-related variables
show the relationship with municipal expenditures that is predicted
by the Current Urban Planning Assumptions. This confirms the
findings of the econometric analysis, which was only able to
explain a relatively small fraction of the cost differences among
communities, and where only one of the growth-related variables
(population density) was found to be statistically significant, but
not practically significant, at conventional confidence
intervals.

Population
Density. The Current Urban Planning Assumptions would
predict that the lowest expenditures per capita would be in the
highest population density quintile. In fact, expenditures per
capita in the highest density quintile were the second highest, and
were above average. Only the lowest density quintile (#5) had
higher municipal expenditures per capita. It should be noted that
the population density of quintile #5 is very low, and below the
general U.S. Census Bureau urbanized area threshold of 1,000
persons per square mile. Expenditures per capita were lower than
average in the middle three quintiles, which are more reflective of
the suburban population densities that have developed in the United
States since 1950. The lowest expenditures per capita were in
quintile #2--the second-highest density quintile. This quintile's
average density is comparatively low--approximately 10 percent
above the average density for the entire database and more than 40
percent lower than the average density of U.S. urbanized areas with
populations over 500,000 in 1950.23 The implication is that higher density
does not lower local government expenditures per capita.

Population
Growth Rate. The actual expenditure data indicate that
quintile #1 (which has the lowest population growth rate) has the
second highest expenditures per capita--at a level above the
average. Like the population density conclusion, the actual
spending data are inconsistent with what would be expected based
upon the Current Urban Planning Assumptions.

Municipality
Age. Municipality age provides the most stark
inconsistency with the Current Urban Planning Assumptions. The
oldest municipalities (quintile #1) have the highest expenditures
per capita, precisely the opposite of what would be expected. The
lowest expenditures per capita are in the newest communities
(quintile #5), which is also the opposite of what the Current Urban
Planning Assumptions would predict.

The
most dense municipalities (quintile #1) also failed to have the
expected lowest wastewater charges per capita or the lowest water
charges per capita. Quintile #1 municipalities did, however, have
lower than average wastewater charges, though only of $7 per capita
per year--hardly rising to the level of "unaffordable." There was
little difference between the quintiles in water charges per
capita. (See Tables 10 and 11 in Appendix.) Thus, the pattern in
the nominal data (actual ranked data) for utilities was different
than predicted by the econometric analysis.

Thus, the actual expenditure data reveal
that more dense, slower growing, and older municipalities do not
have lower expenditures per capita--the opposite of what would be
expected if the Current Urban Planning Assumptions were correct.
24

Other Potential Municipal Expenditure
Drivers

The
fact that the econometric analysis explains so little of the
variation in municipal costs per capita, combined with the fact
that the highest density, slowest growing, and oldest communities
do not have the lower expenditures per capita predicted by the
Current Urban Planning Assumptions, would seem to indicate that
other factors are more important drivers of variation in municipal
costs between communities.

The
most obvious place to look is local government employee
compensation. Employee compensation is by far the largest
expenditure function for most local governments, consuming, on
average, 64 percent of total current expenditures.25 Employee compensation
is approximately 3.5 times capital expenditures.26

Employee compensation varies significantly
between jurisdictions. Census Bureau information indicates that
local government average wages and salaries for similar positions
and skills vary by as much as 93 percent between some states.27 These cost
disparities are not necessarily explained by regional differences.
For example, in the Denver metropolitan area the municipality with
the highest wages and salaries per capita pays nearly 1.5 times the
area average, and more than five times the municipality with the
lowest wages and salaries per capita. Further, there are also
significant differences (up to 123 percent) between the percentage
add-on of employer-paid employee benefits costs among local
governments by state.28

There are other factors that could be
responsible for such large variations. There could be significant
variations between the numbers of hours actually worked by
government employees. This is evident at the state level, where
differences of up to 38 days annually have been shown.29 Thus, it seems likely
that differences in municipal government employee compensation per
capita could be an important factor in explaining differences in
municipal expenditures.30 Finally, there could be significant
variations in the number of employees, or in employee
productivity.

Although the available data cannot be used
to econometrically test the impact of public employee compensation
on municipal costs, the nominal ranking analysis used in the
previous section can be extended to include a review of government
employee compensation. 31 Table
5 provides an estimate of per-capita municipal employee wages and
salaries for each set of quintile rankings for the three urban
planning, growth-related variables.

As
the data in Table 5 illustrate, virtually all of the difference
between the highest municipal expenditure quintile and the lowest
is accounted for (or more than accounted for) by the difference in
municipal employee compensation per capita. This indicates that
differences in employee compensation--not growth factors--may be
the strongest driver of municipal expenditures.

Population
Density. Wages and salaries per capita tend to rise from
quintile 5 (lowest) to quintile 1, which has, by far, the highest
expenditures in the highest density quintile.31

Population
Growth Rate. The highest wage and salary expenditures per
capita are in the slowest growing quintiles (quintiles 1 and 2),
and lowest in the fastest growing quintiles (quintiles 4 and
5).

Municipality
Age. As illustrated in Figure 1, the highest wage and
salary expenditures per capita are in the oldest municipalities
(quintiles 1 and 2), with the lowest expenditures in the newest
municipalities (quintiles 4 and 5).

Each of these conclusions works strongly against
what one might expect from the Current Urban Planning Assumptions.
This is illustrated by reviewing the data for the quintiles under
each Current Urban Planning Assumption that would be expected to
have the lowest expenditures per capita. Table 6 indicates that
differences in employee compensation alone are more than sufficient
to account for the differences in municipal expenditures per
capita--whether by density, population growth, or municipality
age.

Population
Density. The variation from the average in wages and
salaries per capita in the highest density municipalities is larger
(1.34 times) than the variation from the average for the same
municipalities in local government expenditures, as shown in Figure
2.

Population
Growth Rate. The variation from the average in wages and
salaries per capita in the slowest growing municipalities is larger
(2.32 times) than the variation from the average for the same
municipalities in local government expenditures.

Municipality
Age. The variation from the average in wages in salaries
per capita in the oldest municipalities is nearly as large (0.91
times) as the variation from the average for the same
municipalities in local government expenditures. If the average
employer-paid benefits add-on is included, the variation in
employee compensation would be larger than the difference in
expenditures (1.12 times).

In
fact, the impact of increases in local government employee
compensation has been far greater than the sprawl-based costs
projected in Costs of Sprawl--2000. From 1980 to 2000, the gross
additional local government employee compensation alone in the
United States was nearly $2.2 trillion (in 2000 dollars)--or more
than $105 billion per year. This is approximately 12 times the $9.1
billion average annual additional cost projected in Costs of
Sprawl--2000.32

An Alternative Explanation for Differences in
Municipal Spending: Political Entrenchment

The
generally higher spending levels of the older municipalities may be
due to a process of "political entrenchment" that occurs with the
passage of time. The large impact of local government employee
compensation indicates that internal employee interests may be a
principal factor driving municipal expenditures per capita.
According to the nominal ranking analysis presented in Table 7,
there appears to be a strong relationship between higher employee
wages and salaries per capita and higher density, lower population
growth rates, community age, and higher population--all of which
are in opposition to what would be expected if the Current Urban
Planning Assumptions were correct.

Population
Density. The highest wages and salaries quintile has the
highest population density. Densities decrease with each quintile,
with the lowest wages and salaries quintile having the lowest
population density.

Population
Growth Rate. The highest wages and salaries quintile has
the lowest population growth rate. Population growth rates increase
with each quintile, except for the highest growth quintile
(quintile 5). The second-fastest growing quintile (quintile 4) has
the highest population growth rate.

Municipality
Age. The highest wages and salaries quintile has the
oldest average municipality age. Community age decreases with each
quintile, with the lowest wages and salaries quintile being the
youngest.

Perhaps reflecting such entrenchment, older
municipalities have often been notably resistant to cost-effective
management innovations such as privatization, competitive
contracting, more flexible labor arrangements, and innovative
management techniques.33 For example, the oldest quintile of
municipalities had a general government expenditure level 23
percent higher than the youngest (Table 5).

It
must be pointed out, however, that employee compensation is not
likely to be the only cost function that could be exercising undo
special-interest influence on the costs of local governments. Other
political interests not quantified (and perhaps not quantifiable)
may also exercise an impact on municipal spending.

Larger governmental units--which also tend
to be more dense and older34--may be inherently more susceptible to
special-interest capture, whether employee, business, labor, or
other. Generally, it can be expected that the influence of
individual voters would be less in larger jurisdictions and that
special interests would be more likely to exert control. Larger
jurisdictions would seem to provide economies of scale for
lobbying. It would seem reasonable that where there is greater
opportunity for special-interest control, government costs are
likely to be higher. The data in Table 7 indicate that the highest
wages and salaries quintile (quintile 1) has an average population
that is more than 50 percent larger than average, and that the
average population of each succeeding quintile is lower. The lowest
wages and salaries quintile (quintile 5) has the lowest
population--approximately one-half the average. This finding is
counter to another widely held urban planning assumption: that
larger units of government are more cost effective due to economies
of scale.

All
of this seems to indicate that municipal costs are more susceptible
to overwhelming influence by political interests than they are to
economics. Theoretical studies, such as Costs of Sprawl--2000 may
suffer from what might be called the "length of pipe fallacy"--the
assumption that labor rates, cost of materials, and the costs
associated with apparently similar projects is the same in every
local government jurisdiction in a metropolitan area.35 In fact, older,
inner-city government labor rates are often higher than suburban
rates: Overheads may be higher and certainly the operating
environment can be more challenging. For example, expansion of an
inner-city sewer system is likely to be far more costly than laying
a new one in a greenfield area.

"Entrenchment" may have first been noted
by Adam Smith in The Wealth of Nations. He pointed out that
historical control of guilds in the older cities had produced a
situation in which prices were lower in the suburbs, which were
beyond the reach of the guilds. This kept prices in the older
cities above market levels.36 Economist Mancur Olson similarly
postulated that, as time goes on, political and special interests
become more entrenched in older national governments.37 Stronger
bureaucracies, more powerful employee organizations, strong local
business interests, political interests, and more rigid operating
procedures may have developed over a longer time period. These may
force costs in older municipalities to be higher than they would be
in newer municipalities.

An
"entrenchment" theory of municipal finance would be consistent with
the findings of economist Charles Tiebout, who argued that people
tend to "vote with their feet"--to move to newer communities that
better meet their desires and needs. Relative tax levels were an
important component of this thesis, which characterized the new
suburban communities as competing with one another for new
residents.38

Conclusions and Policy Implications

Our
analysis indicates that the Current Urban Planning Assumptions are
of virtually no value in predicting local government expenditures
per capita. The lowest local government expenditures per capita are
not in the higher density, slower growing, and older
municipalities.

On
the contrary, the actual data indicate that the lowest expenditures
per capita tend to be in medium- and lower-density municipalities
(though not the lowest density); medium- and faster-growing
municipalities; and newer municipalities. This is after 50 years of
unprecedented urban decentralization, which seems to be more than
enough time to have developed the purported urban sprawl-related
higher local government expenditures. It seems unlikely that the
higher expenditures that did not develop due to sprawl in the last
50 years will evolve in the next 20--despite predictions to the
contrary in The Costs of Sprawl--2000 research.

It
seems much more likely that the differences in municipal
expenditures per capita are the result of political, rather than
economic factors, especially the influence of special
interests.

Wendell Cox, Principal of
the Wendell Cox Consultancy in metropolitan St. Louis, is a
Visiting Fellow at The Heritage Foundation and a Visiting Professor
at the Conservatoire National des Arts et Metiers in Paris. Joshua
Utt is a Ph.D. candidate in Economics at Washington State
University and an Adjunct Fellow at the Discovery Institute in
Seattle, Washington.

APPENDIX

1.Urban sprawl is often
thought of as an American phenomenon. In fact, sprawl has been
occurring throughout the high-income world and much of the low- and
middle-income world. Virtually all population growth in major
Western European urban areas has been outside the urban cores for
at least three decades, occurring mostly in suburban style
settings.

9.This is not the case
for other local units of general government, such as counties and
townships. These generally include much rural (non-urban) land. As
a result, density data for other local government units is not
reflective of urban densities.

10.Metropolitan areas of
more than 1,000,000 residents comprised approximately 58 percent of
the nation's population in 2000 (2000 metropolitan definitions).
The 49 such areas had a combined population of 163 million, out of
a national total of 281 million (Table H-10).

11.As a result,
jurisdictions such as New York, Philadelphia, San Francisco,
Baltimore, St. Louis, Miami, New Orleans, Indianapolis, and
Lexington, Kentucky were excluded.

12.Byron Schlomach and
Wendell Cox, A Look at School Facilities Funding in Texas, Texas
Public Policy Foundation, 2004 at
http://www.texaspolicy.com/pdf/2004-04-facilities.pdf (June 15,
2004).

13.This research uses
multi-linear regression analysis. Independent variables (such as
population density) were chosen. These were theorized to have some
impact on municipal expenditures per capita (the dependent
variable).

14.Water and wastewater
utilities in France also tend to be privately owned.

15.Current expenditures
are the day-to-day costs of operations, such as employee
compensation, materials and supplies, and professional service
contracts. Capital expenditures are for construction and
acquisition of assets, such as vehicles, data processing equipment,
furniture, etc.

16.The municipalities in
the sample contained 63 million people in 2000 (39 percent of the
total population in metropolitan areas over 1,000,000).

17.R2=0.29.

18.This factor (local
direct general expenditures as a percentage of state government
plus local government direct general expenditures) was included to
capture the differences (by state) in expenditure distribution
between state and local governments.

19.Negative or
positive.

20.Average population
density of the sample was 3,776 per square mile; a 1,000 increase
in population density would increase densities by more than 25
percent.

21.There have been
virtually no density increases of such a magnitude in
municipalities that do not have broad expanses of undeveloped
space.

22.There were 764
wastewater observations (Waste

23.Calculated from U.S.
Census Bureau data.

24.It has been suggested
by some that older, more densely populated municipalities subsidize
newer, more suburban municipalities. In fact, however, the nominal
analysis indicates the opposite. The quintile of municipalities
with the highest state and federal aid per capita average 45 years
old ($852 annually per capita). This is nearly four times that of
the second quintile (36 years and $218). The three lowest state and
federal aid quintiles have average ages of from 26 to 31 years.

25.Calculated from U.S.
Census Bureau data for 2000, adding the state and local government
employer-paid employee benefits factor calculated from the National
Income and Product Accounts.

26.Includes the cost of
building new water and wastewater systems.

27.Calculated from U.S.
Census Bureau 2001 government employee database.

28.The latest available
comprehensive information on local government employer-paid
employee benefits was the 1987 U.S. Census of Governments, from
which this figure was calculated.

30.It is also likely that
differences in hourly employee compensation per capita would be an
important determinant of differences in other government total
expenditures, such as at the county, school district, township (and
comparable governments) and special district levels.

31.Employee compensation
is estimated using the gross local government wages and salaries
data from the Census Bureau database, scaled downward to exclude
utilities and education and increased by the average 24.5 percent
cost of employer paid employee benefits. Because wastewater and
water expenditures are small compared to overall municipal
expenditures, it was not considered reliable to estimate wages and
salaries for these functions using the same formula.

32.Estimated from U.S.
Department of Commerce data. Assumes a constant rate of annual
increase from 1980 to 2000.

33.This is illustrated by
the case of public transit. In 2001, none of the approximately 100
older transit systems (established before 1980 or descended from
pre-1980 systems) in major metropolitan areas competitively
contracted their bus systems. By contrast, 56 percent of the newer,
largely suburban systems competitively contract their bus systems.
See Wendell Cox, Performance Measures in Urban Public Transport,
paper presented to the 8th International Conference on Competitive
and Ownership in Public Transport, Rio de Janeiro, 2003, at
www.publicpurpose.com/t8-gbc.pdf (June 15, 2004).

34.This research indicates
that the highest population quintile also has the highest
population density and the highest expenditures per capita.

35.For example, in Los
Angeles--where many transit services are sponsored by newer,
suburban agencies--costs per hour of service are 46 percent lower
where provided under contract by agencies other than the core
transit system Wendell Cox, Competitive Participation in U.S.
Public Transport: Special Interests Versus the Public Interest,
paper presented to the 8th International Conference on Competition
and Ownership in Land Passenger Transport, Rio de Janeiro, 2003, at
www.publicpurpose.com/
t8-cc.pdf (June 15, 2004).

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The anti-sprawl movement claims that newer (more sprawling)communities have higher government costs than older (lesssprawling) communities. However, an econometric analysis shows nopractically significant difference in expenditures per capitabetween the two types of communities. Other factors explain thevariation in municipal expenditures.

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